Monitoring patients in hospital beds using unobtrusive depth sensors.
Monitoring patients in hospital beds using unobtrusive depth sensors.
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使用不显眼的深度传感器监测医院病床上的患者。
DOI：
10.1109/embc.2014.6944972
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Rantz,Marilyn
中科院分区：
文献类型：
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作者：
Banerjee,Tanvi;Enayati,Moein;Keller,JamesM;Skubic,Marjorie;Popescu,Mihail;Rantz,Marilyn
我们提出了一种使用Kinect传感器收集的深度数据来识别医院房间中患者活动的方法。深度传感器，如Kinect，确保白天和晚上都可以进行活动分割，同时解决患者的隐私问题。它还提供了一种以非侵入性方式远程监控患者的技术。现有的跌倒检测算法目前正在密苏里大学医院(MUH)的几个房间内生成跌倒警报。在这篇文章中，我们描述了一种减少错误警报的技术，例如枕头从床上掉下来或设备移动。我们通过在生成跌倒警报的时间内检测患者在床上的存在来实现这一点。我们测试了我们的算法在两个医院病房中获得的96个小时。
We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a non-intrusive manner. An existing fall detection algorithm is currently generating fall alerts in several rooms in the University of Missouri Hospital (MUH). In this paper we describe a technique to reduce false alerts such as pillows falling off the bed or equipment movement. We do so by detecting the presence of the patient in the bed for the times when the fall alert is generated. We test our algorithm on 96 hours obtained in two hospital rooms from MUH.